{"id":"https://openalex.org/W2902070858","doi":"https://doi.org/10.23919/eusipco.2018.8553236","title":"CycleGAN-VC: Non-parallel Voice Conversion Using Cycle-Consistent Adversarial Networks","display_name":"CycleGAN-VC: Non-parallel Voice Conversion Using Cycle-Consistent Adversarial Networks","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2902070858","doi":"https://doi.org/10.23919/eusipco.2018.8553236","mag":"2902070858"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco.2018.8553236","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2018.8553236","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 26th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020693766","display_name":"Takuhiro Kaneko","orcid":"https://orcid.org/0009-0000-8016-5144"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Takuhiro Kaneko","raw_affiliation_strings":["NTT Communication Science Laboratories, NTT Corporation, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, NTT Corporation, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001243214","display_name":"Hirokazu Kameoka","orcid":"https://orcid.org/0000-0003-3102-0162"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hirokazu Kameoka","raw_affiliation_strings":["NTT Communication Science Laboratories, NTT Corporation, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, NTT Corporation, Japan","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5020693766"],"corresponding_institution_ids":["https://openalex.org/I2251713219"],"apc_list":null,"apc_paid":null,"fwci":24.5455,"has_fulltext":false,"cited_by_count":351,"citation_normalized_percentile":{"value":0.9953488,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2100","last_page":"2104"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7839130163192749},{"id":"https://openalex.org/keywords/identity","display_name":"Identity (music)","score":0.5067160725593567},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.5064181089401245},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46660611033439636},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.45499923825263977},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.447208970785141},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4411216080188751},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.43638864159584045},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.43409788608551025},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4188443124294281},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4182620048522949},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33570677042007446},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.13717120885849}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7839130163192749},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.5067160725593567},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.5064181089401245},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46660611033439636},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45499923825263977},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.447208970785141},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4411216080188751},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.43638864159584045},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.43409788608551025},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4188443124294281},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4182620048522949},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33570677042007446},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.13717120885849},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/eusipco.2018.8553236","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2018.8553236","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 26th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":74,"referenced_works":["https://openalex.org/W10800834","https://openalex.org/W49412823","https://openalex.org/W142801778","https://openalex.org/W1509691205","https://openalex.org/W1522301498","https://openalex.org/W1523372075","https://openalex.org/W1588266896","https://openalex.org/W1665214252","https://openalex.org/W1903029394","https://openalex.org/W1921523184","https://openalex.org/W1974745215","https://openalex.org/W1977362459","https://openalex.org/W1987992317","https://openalex.org/W2022125261","https://openalex.org/W2056852181","https://openalex.org/W2057609679","https://openalex.org/W2086796102","https://openalex.org/W2099471712","https://openalex.org/W2105160541","https://openalex.org/W2120605154","https://openalex.org/W2123003832","https://openalex.org/W2123771434","https://openalex.org/W2127520494","https://openalex.org/W2142300631","https://openalex.org/W2148846882","https://openalex.org/W2153057929","https://openalex.org/W2156142001","https://openalex.org/W2157412983","https://openalex.org/W2161727827","https://openalex.org/W2169579015","https://openalex.org/W2194775991","https://openalex.org/W2331128040","https://openalex.org/W2396025094","https://openalex.org/W2471520273","https://openalex.org/W2473388484","https://openalex.org/W2474531669","https://openalex.org/W2475998840","https://openalex.org/W2476548250","https://openalex.org/W2502312327","https://openalex.org/W2515028311","https://openalex.org/W2518312472","https://openalex.org/W2532494225","https://openalex.org/W2567070169","https://openalex.org/W2586453598","https://openalex.org/W2593414223","https://openalex.org/W2598581049","https://openalex.org/W2651834199","https://openalex.org/W2658996865","https://openalex.org/W2666408839","https://openalex.org/W2746654391","https://openalex.org/W2747744257","https://openalex.org/W2774848319","https://openalex.org/W2787748320","https://openalex.org/W2951939904","https://openalex.org/W2962793481","https://openalex.org/W2962896155","https://openalex.org/W2963444790","https://openalex.org/W2963970792","https://openalex.org/W2964121744","https://openalex.org/W2964341837","https://openalex.org/W4293398859","https://openalex.org/W4320013936","https://openalex.org/W6602007935","https://openalex.org/W6631190155","https://openalex.org/W6631309588","https://openalex.org/W6635216677","https://openalex.org/W6637242042","https://openalex.org/W6640185926","https://openalex.org/W6684957941","https://openalex.org/W6702130928","https://openalex.org/W6711854987","https://openalex.org/W6730095352","https://openalex.org/W6731370813","https://openalex.org/W6735204497"],"related_works":["https://openalex.org/W1978572805","https://openalex.org/W2383807498","https://openalex.org/W1997992934","https://openalex.org/W1987225439","https://openalex.org/W4238188170","https://openalex.org/W2125114371","https://openalex.org/W2019977573","https://openalex.org/W2149980199","https://openalex.org/W3125766170","https://openalex.org/W2054007705"],"abstract_inverted_index":{"We":[0,139,168],"propose":[1],"a":[2,10,52,141,173,223],"non-parallel":[3,91,174],"voice-conversion":[4],"(VC)":[5],"method":[6,24,171,229],"that":[7,29,134,181,210,220],"can":[8,97],"learn":[9],"mapping":[11,156],"from":[12,90],"source":[13],"to":[14,84,103,118,158,219],"target":[15,105],"speech":[16,101,216],"without":[17,39,112],"relying":[18],"on":[19,107,172],"parallel":[20,227],"data.":[21,92],"The":[22],"proposed":[23],"is":[25,31,233],"particularly":[26],"noteworthy":[27],"in":[28,127,189],"it":[30,82,148],"general":[32],"purpose":[33],"and":[34,37,63,71,77,146,161,194,239],"high":[35],"quality":[36,212],"works":[38],"any":[40],"extra":[41],"data,":[42],"modules,":[43],"or":[44],"alignment":[45],"procedure.":[46],"Our":[47],"method,":[48],"called":[49],"CycleGAN-VC,":[50],"uses":[51],"cycle-consistent":[53],"adversarial":[54,76,95],"network":[55],"(CycleGAN)":[56],"with":[57,143,149,203,222],"gated":[58,144],"convolutional":[59],"neural":[60],"networks":[61],"(CNNs)":[62],"an":[64,86,150],"identity-mapping":[65,151],"loss.":[66,152],"A":[67,206],"CycleGAN":[68,142],"learns":[69],"forward":[70],"inverse":[72],"mappings":[73],"simultaneously":[74],"using":[75],"cycle-consistency":[78],"losses.":[79],"This":[80,116,153],"makes":[81],"possible":[83],"find":[85],"optimal":[87],"pseudo":[88],"pair":[89],"Furthermore,":[93],"the":[94,99,104,108,155,182,211,214,241],"loss":[96],"bring":[98],"converted":[100,183,215],"close":[102],"one":[106],"basis":[109],"of":[110,191,213,243],"indistinguishability":[111],"explicit":[113],"density":[114],"estimation.":[115],"allows":[117,154],"avoid":[119],"over-smoothing":[120],"caused":[121],"by":[122],"statistical":[123,130],"averaging,":[124],"which":[125,197],"occurs":[126],"many":[128],"conventional":[129],"model-based":[131,226],"VC":[132,175,228],"methods":[133],"represent":[135],"data":[136],"distribution":[137],"explicitly.":[138],"configure":[140],"CNNs":[145],"train":[147],"function":[157],"capture":[159],"sequential":[160],"hierarchical":[162],"structures":[163],"while":[164],"preserving":[165],"linguistic":[166],"information.":[167],"evaluated":[169],"our":[170],"task.":[176],"An":[177],"objective":[178],"evaluation":[179,208],"showed":[180,209],"feature":[184],"sequence":[185],"was":[186,217],"near":[187],"natural":[188],"terms":[190],"global":[192],"variance":[193],"modulation":[195],"spectra,":[196],"are":[198],"structural":[199],"indicators":[200],"highly":[201],"correlated":[202],"subjective":[204,207],"evaluation.":[205],"comparable":[218],"obtained":[221],"Gaussian":[224],"mixture":[225],"even":[230],"though":[231],"CycleGAN-VC":[232],"trained":[234],"under":[235],"disadvantageous":[236],"conditions":[237],"(non-parallel":[238],"half":[240],"amount":[242],"data).":[244]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":35},{"year":2024,"cited_by_count":46},{"year":2023,"cited_by_count":62},{"year":2022,"cited_by_count":55},{"year":2021,"cited_by_count":71},{"year":2020,"cited_by_count":54},{"year":2019,"cited_by_count":19},{"year":2018,"cited_by_count":1}],"updated_date":"2026-05-16T08:24:45.110214","created_date":"2025-10-10T00:00:00"}